Figure REF  shows the median win rate for our proposed algorithm and baselines. We use an \(\epsilon \) -greedy policy in which \(\epsilon \)  decreases from 1 to 0.05
over 0.5 million timesteps in 3s_vs_5z and 2c_vs_64zg, and over 1 million timesteps in 3s5z_vs_3s6z and corridor for all algorithms.
We can see that LFF significantly outperforms baselines, indicating that sharing intention via the leader-follower forest can improve learning speed and achieve better coordination
in practice.
To further demonstrate the effectiveness of our method,
we also combine communication protocols with QMIX [1]}, which is a popular credit assignment algorithm. In this setting,
LFF-IS still outperforms our baselines. The results can be found in Appendix.
